Random number generators are used in various fields including network security, cryptography, computer simulations, statistical sampling, and other fields where producing an unpredictable result is desirable. The randomness of an output of a random number generator can be determined using statistical tests that measure the unpredictability of the output. Some random number generators are referred to as pseudo-random number generators and produce pseudo-random outputs that are predictable for a given input. For example, a computer program that uses a mathematical formula to produce a seemingly random output is a pseudo-random number generator if the seemingly random output is always the same (and therefore predictable) for a given input. Other random number generators are referred to as true-random number generators, which may generate a truly random output, such as based on a physical phenomenon that is believed to be random. Examples of physical phenomenon include atmospheric noise, thermal noise, and other external electromagnetic and quantum phenomena. However, even when relying on such physical phenomena, the randomness of the output of a true-random number generator can be affected by errors or biases, such as when measuring the physical phenomenon. Errors that cause the output to be predictable can be referred to as deterministic errors. In some case, these deterministic errors can be mitigated by calibrating the true-random number generator based on a statistical analysis of outputs of the true-random number generator. In some examples, calibrating a true-random number generator takes time, only last for a period of time, and introduces pseudo-randomness by applying a mathematical formula.